We often make high stakes choices based on complex information that we have no way to verify. Careful Bayesian reasoning—assessing every reason why a claim could be false or misleading—is not feasible, so we necessarily act on faith: we trust certain sources and treat claims as if they were direct observations of payoff relevant events. This creates a challenge when trusted sources conflict: Practically speaking, is there a principled way to update beliefs in response to contradictory claims? I propose a model of belief formation along with several updating axioms. An impossibility theorem shows there is no obvious best answer, while a representation theorem delineates the boundary of what is possible.

中文翻译：

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从矛盾的证据更新信仰的实用指南

我们经常根据无法验证的复杂信息做出高风险的选择。谨慎的贝叶斯推理-评估索赔可能是虚假或误导性的所有原因-是不可行的，因此我们必须基于信念行事：我们信任某些来源，并将索赔视为对付款相关事件的直接观察。当受信任的来源发生冲突时，这会带来挑战：实际上，是否存在一种原则上的方法来响应矛盾的主张而更新信念？我提出了一种信念形成模型以及一些更新的公理。不可能定理表明没有明显的最佳答案，而表示定理则描述了可能的边界。